Research on a recommendation model for sustainable innovative teaching of Chinese as a foreign language based on the data mining algorithm

With the continuous development of teaching Chinese as a foreign language, more teaching methods are combined with network teaching. However, it is difficult for network teaching methods to find ways that are suitable for different learners from various teaching resources. Therefore, to help learners obtain appropriate teaching methods from the network teaching platform, the research establishes a network teaching recommendation model for Chinese as a foreign language based on the user's interest similarity. Three experimental schemes are designed to verify the effect of the proposed model. The experimental results show that the mean absolute error (MAE) scores of the model in the three schemes are 0.67, 0.7095, and 0.7428, respectively; the RMSE scores are 0.88, 0.9346, and 0.9695, respectively. Thus, the proposed collaborative filtering recommendation algorithm based on user interest similarity migration has good recommendation performance.